1 to 25 of 135 Pandas Jobs in the UK excluding London

Snowflake, AWS, and Python Work directly with investment professionals to scope and deliver solutions What we’re looking for: Strong Python engineering skills, including use of data libraries like pandas and NumPy Experience working in front office environments Familiarity with data pipelines and cloud infrastructure Pragmatic, detail-oriented, and able to work independently Experienced Python-focused Software/Data Engineer More ❯

utilising cloud platforms such as AWS, Azure and Snowflake for scalable deployment. Key Responsibilities: Develop and optimise quantitative models and analytical tools in Python Leverage statistical g packages (e.g., pandas, NumPy) for data analysis and modelling Work closely with portfolio managers, researchers, and technology teams to deploy robust, scalable solutions Design and implement data pipelines and workflows utilising AWS or More ❯

years of hands-on experience. You’ll help streamline client onboarding, build robust integrations, and play a key role in evolving the platform. Tech Stack & Methodology Python (Dataclasses, Pandas, Asyncio) PostgreSQL, AWS (ECS, Lambda, SQS) Agile (XP), TDD, CI/CD, and strong automation culture If you're familiar with these tools—or eager to dive in—this is the More ❯

manipulation, asynchronous programming, and performance optimization. Experience with distributed systems, parallel computing, and high-performance processing of large datasets. Strong experience in data pipelines, working with tools such as Pandas, NumPy, and SQL/NoSQL databases. Proven experience working in fast-paced environments, ideally within trading, financial services, or high-frequency environments. Proficiency in developing RESTful APIs, microservices, and working More ❯

Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working More ❯

functional teams to analyze requirements and architect high-quality software solutions. Develop IT solutions using Python and related libraries for AI applications. Build scalable data pipelines utilizing technologies like Pandas, NumPy, or Spark. Apply Agile and DevOps practices to deliver effective solutions. Understand and translate business requirements into technical specifications. Identify opportunities for enhancing DevOps processes and explore new technologies. More ❯

functional teams to enhance testing practices across cloud-native microservices and data platforms. You'll develop scalable test automation frameworks with a focus on data validation using tools like Pandas, and lead strategies for testing data lakes. Collaborate closely with QA and Java/Spring Boot development teams to ensure data quality and reliability across our sports technology ecosystem. What … Your Day Will Look Like Build scalable and robust frameworks for validating complex data systems, pipelines, and APIs using tools like Pandas, Great Expectations, and Java modules. Collaborate with development teams to design and implement automated tests for microservices, emphasizing Spring Boot and Java-based architectures. Implement testing strategies for AWS data lakes (e.g., S3, Glue, Athena) with a focus … methodologies and train teams to independently implement and use the automation frameworks. Skills, Knowledge, and Expertise Expertise in validating complex datasets, creating synthetic test data, and leveraging tools like Pandas and Python for comprehensive data quality assurance. Proven ability to build and maintain scalable test automation frameworks, with a focus on backend, API, and data systems using tools like Pytest More ❯

functional teams to enhance testing practices across cloud-native microservices and data platforms. You'll develop scalable test automation frameworks with a focus on data validation using tools like Pandas, and lead strategies for testing data lakes. Collaborate closely with QA and Java/Spring Boot development teams to ensure data quality and reliability across our sports technology ecosystem. What … Your Day Will Look Like Build scalable and robust frameworks for validating complex data systems, pipelines, and APIs using tools like Pandas, Great Expectations, and Java modules. Collaborate with development teams to design and implement automated tests for microservices, emphasizing Spring Boot and Java-based architectures. Implement testing strategies for AWS data lakes (e.g., S3, Glue, Athena) with a focus … methodologies and train teams to independently implement and use the automation frameworks. Skills, Knowledge, and Expertise Expertise in validating complex datasets, creating synthetic test data, and leveraging tools like Pandas and Python for comprehensive data quality assurance. Proven ability to build and maintain scalable test automation frameworks, with a focus on backend, API, and data systems using tools like Pytest More ❯

Building from the beginning – lots of IT Challenges – Python Code Migrating from library, tweaking, methodology. Skills: Quant Developer Python: Blackrock numeric data processing: numpy, scipy text data processing: pyarrow, pandas databases: sqlalchemy, psycopg parallelization: multiprocessing, concurrent.futures More ❯

experience of Bedrock, Sagemaker, Python and Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Key Skills & Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline … OCR Use-Case Development: Proficiency with Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests More ❯

experience of Bedrock, Sagemaker, Python and Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Key Skills & Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline … OCR Use-Case Development: Proficiency with Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests More ❯

data is stored, organized, and accessed in the most efficient and effective manner. Your Toolkit The Languages You Speak : Python, SQL, the dialect of data. Libraries Tools : Terraform, Flask, Pandas, FastAPI, Dagster, GraphQL, SQLAlchemy, GitLab, Athena. Your Trusted Companions : Docker, Snowflake, MongoDB, Relational Databases (eg MySQL, PostgreSQL), Dagster, Airflow/Luigi, Spark, Kubernetes. Your AWS Kingdom : Lambda, Redshift, EC2, ELB … Philosophy : Continuous integration/deployments, (CI/CD) automation, rigorous code reviews, documentation as communication. Preferred Qualifications Familiar with data manipulation and experience with Python libraries like Flask, FastAPI, Pandas, PySpark, PyTorch, to name a few. Proficiency in statistics and/or machine learning libraries like NumPy, matplotlib, seaborn, scikit-learn, etc. Experience in building ETL/ELT processes and More ❯

Responsibilities: - AWS Data Science Tools: Hands-on with SageMaker, Lambda, Step Functions, S3, Athena. - OCR Development: Experience with Amazon Textract, Tesseract, and LLM-based OCR. - Python Expertise: Skilled in Pandas, NumPy, scikit-learn, PyTorch, Hugging Face Transformers; modular, testable code. - ML Models: Proficient in regression, classification, clustering, and time-series forecasting. - Business Insight: Translate business needs into data-driven solutions More ❯

pySpark and SQL for data mining; additional experience and knowledge of Big Data tools preferred. Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and More ❯

Processing team. Join a small, ambitious team working closely with Java and Data Science teams to build and support infrastructure for Credit Risk Management, using technologies like FastAPI, Pandera, Pandas, and Pydantic. The role involves collaboration, architecture development, code review, and supporting data science infrastructure. Remote work is possible with occasional office visits; based anywhere in the UK with the More ❯

years of professional software development with exposure to capital markets or trading (equities and/or execution). Technical Skills: Proficient in Python with some exposure to data analysis (Pandas, NumPy) or similar (R, MATLAB). Solid background in object-oriented programming (Java preferred; C++ or C# also valued). Familiarity with Linux-based systems (configuration, scripting, deployment). Docker More ❯

Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of SQL, NoSQL, and data modeling. Familiarity with More ❯

or data engineering. Ability to work standard European time-zone hours and legal authorisation to work in your country of residence. Strong experience with Python’s data ecosystem (e.g., Pandas, NumPy) and deep expertise in SQL for building robust data extraction, transformation, and analysis pipelines. Hands-on experience with big data processing frameworks such as Apache Spark, Databricks, or Snowflake More ❯

model performance evaluation, hyperparameter tuning, and maintenance using tools like Vertex AI Pipelines. Cloud Computing (Google Cloud Platform - GCP Preferred) Technical Expertise & Tools Python: Advanced proficiency in data analysis (Pandas, NumPy), machine learning, PI development (Flask/FastAPI), and writing clean, maintainable code. SQL: Expertise in querying, database design/optimization, stored procedures, functions, partitioning/clustering strategies for BigQuery More ❯

approaches. Deployment: ship a model as a container, update an Airflow (or Azure Data Factory) job. Review: inspect dashboards, compare control vs. treatment, plan next experiment. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) SQL (Redshift, Snowflake or similar) AWS SageMaker → Azure ML migration, with Docker, Git, Terraform, Airflow/ADF Optional extras: Spark, Databricks, Kubernetes. What you … yrs optimisation/recommender work at production scale (dynamic pricing, yield, marketplace matching). Mathematical optimisation know-how - LP/MIP, heuristics, constraint tuning, objective-function design. Python toolbox: pandas, NumPy, scikit-learn, PyTorch/TensorFlow; clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for More ❯

Designing Generative AI Proof of Concept. Contribute to AI infrastructure Building reliable, scalable, and flexible systems. Influence Opinion and decision-making across AI and ML Skills Python SQL/Pandas/Snowflake/Elasticsearch Docker/Kubernetes Airflow/Spark Familiarity with GenAI models/libraries Requirements 4+ years of relevant software engineering experience post-graduation A degree (ideally a More ❯

is looking for a Data Scientist to join its innovative team. This role requires hands-on experience with machine learning techniques and proficiency in data manipulation libraries such as Pandas, Spark, and SQL. As a Data Scientist at PwC, you will work on cutting-edge projects, using data to drive strategic insights and business decisions. If you have strong analytical … development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use Azure/GCP/AWS). Skills we'd also like to hear about: Evidence of modelling experience applied More ❯

is looking for a Data Scientist to join its innovative team. This role requires hands-on experience with machine learning techniques and proficiency in data manipulation libraries such as Pandas, Spark, and SQL. As a Data Scientist at PwC, you will work on cutting-edge projects, using data to drive strategic insights and business decisions. If you have strong analytical … development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use Azure/GCP/AWS). Skills we'd also like to hear about: Evidence of modelling experience applied More ❯

is looking for a Data Scientist to join its innovative team. This role requires hands-on experience with machine learning techniques and proficiency in data manipulation libraries such as Pandas, Spark, and SQL. As a Data Scientist at PwC, you will work on cutting-edge projects, using data to drive strategic insights and business decisions. If you have strong analytical … development (Machine learning frameworks and tooling e.g. Sklearn) and (Deep learning frameworks such as Pytorch and Tensorflow). Understanding of machine learning techniques. Experience with data manipulation libraries (e.g. Pandas, Spark, SQL). Git for version control. Cloud experience (we use Azure/GCP/AWS). Skills we'd also like to hear about: Evidence of modelling experience applied More ❯

the design and development of advanced AI and machine learning models, integrating plasma physics insights to innovate semiconductor solutions, such as plasma etch and deposition. Utilize Python libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch) to architect and prototype cutting-edge ML systems, leveraging Linux environments. Able to expand programming expertise to other languages such as C++ and Java. Drive … testing, and experience leading mid-scale development projects. Advanced proficiency in at least one of Python, C++, Fortran, Julia, or Java, with experience in AI/ML libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch) and familiarity with Linux environments. Strong understanding of physics and engineering principles, ideally with plasma physics knowledge relevant to semiconductor processing. Exceptional communication and leadership More ❯